Re-vamp edge computing
Modernizing data and its management can be possible only if organizations adopt edge computing and re-vamp the network infrastructure. Managing data at the edge involves extracting relevant data from a multitude of sources, storing and historizing that data, and ensuring data quality and security. A robust, flexible, and agile edge is required to make data accessible to a diverse group on a global scale. The traditional wide-area networking (WAN) based hub-and-spoke model is less flexible and scalable, and will not suffice to transport data from the edge to centralized data centers. Data modernization and edge transformation thus need to happen hand in hand.
Performance at the edge, data at the center
It is now the age of edge computing. Clouds and data centers have been the focal point for several years, but as connected devices, generating data at the edge grow, how well IT teams manage edge computing will determine business success. Unlocking the full potential of digital transformation at the edge will require IT functions to undergo a transformation.
Storing data at the edge, however, can be risky because it is most vulnerable to downtime caused by any eventuality. IT teams thus back up data to another site for operational resiliency, but this method is both time and cost-prohibitive. Factor in the sheer volume of data pouring in from an array of connected devices at the edge, IoT sensors that need data processing in real-time, etc., and sending large volumes of data over bandwidth-challenged networks at the edge becomes a nightmare.
Software-defined WANs, or SD-WANs, are resolving this issue to an extent with their inherent low latency and high speed, thus allowing data to be centralized yet instantly and easily accessible from anywhere. The added advantage is that all this data and its travel from the edge to the center is very secure and since data backup is managed centrally with any new data from the edge being immediately synced to the main storage location for easy data recovery.
Managing Data at the Edge
The principle behind data management at the edge is simple: store data closer to where it’s generated and eliminate the need to send it all over the network to a centralized resource. This is a blessing for applications that require data to be stored and seen instantly with no latency or low connectivity obstructing accessibility. Businesses can be made transaction-based and tactical decisions readily based on the real-time data available at the edge. Organizations, such as oil mining corporations, are also demanding technology that allows them to process and analyze data at the edge so that they can use those insights – such as temperature readings from a specific drilling site – to take immediate action. Analytics-oriented edge data involving even deep analytics is the next new trend in data management at the edge.
Of course, this poses an architectural challenge in terms of storing all that data. Some organizations are thus considering data partitioning as a way to store and maintain the tons of data generated at the edge. Data partitioning will be determined by data usage patterns at the edge, data governance and security, as well as physical storage requirements. In addition to these factors, other points to consider for data management at the edge are the limitations involved in upgrading edge-based devices and the type of database technology being used.
There are quite a few challenges to efficient data management at the edge, including upgrades that require technicians to be physically present and monitoring data center health across locations. It requires a combination of remote management tools, analytic capabilities, and databases. But despite these challenges and complexities, edge data centers offer a high-performance and cost-effective means to maintains smooth functionality and make data easily accessible to users. Migrating data management to the edge can help organizations benefit from the rising number of IoT devices at the edge, enhance network speeds, and improve customer experiences. The scalable nature of managing data at the edge makes it an ideal choice for fast-growing organizations that need agility and speed.